A hybrid feature selection scheme for unsupervised learning and its application in bearing fault diagnosis

Expert Systems with Applications

Volumn 38, Issue 9, 2011, Pages 11311-11320

  Yang, Yang ^a   Liao, Yinxia ^b   Meng, Guang ^a   Lee, Jay ^b  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b UNIVERSITY OF CINCINNATI   (United States)

Author keywords

Fault diagnostics; Feature selection; Unsupervised learning

Indexed keywords

BEARING FAULT DIAGNOSIS; BEARING FAULT DIAGNOSTICS; CONDITION BASED MAINTENANCE; CONDITION MONITORING SYSTEMS; FALSE-ALARM RATES; FAULT DIAGNOSTICS; FEATURE SELECTION; FEATURES SELECTION; HYBRID FEATURES; MACHINE LEARNING METHODS; MULTIPLE CLUSTERINGS; REAL WORLD SITUATIONS; SIMILARITY MEASUREMENTS;

ALARM SYSTEMS; BEARINGS (STRUCTURAL); CONDITION MONITORING; UNSUPERVISED LEARNING;

FEATURE EXTRACTION;

EID: 79955612833     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.02.181     Document Type: Article

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